In radiological practice, geometric measurements are frequently used to aid diagnosis of abnormalities. In order to perform these measurements, key user points must be placed in an image, for example in an image displayed on a display device, on their corresponding anatomical landmark position. Measurements such as the distance between two points, or the angulation between lines are based on the position of these key user points. Furthermore, the geometry as a whole may be assessed for normality or abnormality, involving an analysis of the complete shape. Hence there is a need to automate and objectify the extraction of quantitative information that is embedded in a radiological image.
An example of such a frequently performed measurement is the computation of the cardiothoracic ratio (CTR) in thorax RX images (FIG. 6). This ratio is defined as the ratio of the transverse diameter of the heart, at the level of the apex, to the internal diameter of the thorax (ID), i.e. CTR=(MLD+MRD)/ID.
The transverse diameter of the heart is composed of the maximum transverse diameter on the left side of the heart (MLD) and the maximum transverse diameter on the right side of the heart (MRD). Clearly, this definition entails that the radiologist searches along the inner border of the left and right ribcage boundary to locate the point pair needed to compute the internal diameter ID. This point pair must lie at the greatest internal diameter of the thorax. Likewise, the left and right heart shadow border must be searched to locate the points needed to compute the sum of MLD and MRD. More specifically, these points are situated most distant with respect to the midline of the spine. The process of border search requires that the radiologist is performing anatomy segmentation and locating the points (a total of four in this example) on the segmented anatomy. The segmentation step, in the case of CTR computation, amounts to delineating the lung fields.
Many other measurements in digital images follow a similar approach involving the segmentation (224,418) of the anatomic organ or entity onto which segmented geometry the characteristic points (510) and measurement objects (512) are determined and finally measurements (514) are performed (FIG. 5).
Referring to the example of cardiothoracic index calculation, to automatically position the required points, a method is needed to automatically segment the lung fields on a chest radiographic image.
The segmentation problem can be approached in several ways, depending on the application. Segmentation strategies evolved from low-level strategies in the early years of computer vision to the more recent model-based strategies.
Low-level methods rely on local image operators separating pixels with different photometric characteristics and grouping of pixels with similar local photometric characteristics. Examples of both classes are edge detection and region growing. Despite the poor performance of these low-level approaches, they are very popular in most commercial image analysis tools. The main reasons are that they are simple to understand and to implement. For complex image data however, such as present in medical images and exemplified by the content of a thorax image as described above, their usefulness is limited.
More successful methods incorporate a priori knowledge about the shape to be segmented and about the photometric or gray-level appearance of the object in the image. These methods, referred to as model-based methods are often based on template matching. A template is matched for instance by correlation or with generalized Hough transform techniques. Unfortunately, the template matching is likely to fail in case of medical images. This is due to the large variability in shape and gray-level appearance that the anatomic object may exhibit.
Methods based on active contours, introduced by Kass et. al. (M. Kass, A. Witkin, and D. Terzopoulos, Snakes: active contour models, Int. J. Computer Vision, 1(4):321-331, 1988) and level sets (J. A. Sethian, Level set methods and fast marching methods, Cambridge Univ. Press, Cambridge, U.K. 1999) are able to cope with a larger shape variability, but are still unsuited for many medical segmentation tasks because little a priori knowledge about the object to be segmented can be incorporated. Handcrafted parametric models overcome this problem, but are limited to a single application.
In view of these shortcomings, it is obvious that there is need for a generic segmentation scheme that can be trained with examples in order to acquire knowledge about the shape of the object to be segmented and the gray-level appearance of the object in the image. Active shape models (ASMs), introduced by Cootes and Taylor (T. F. Cootes, C. J. Taylor, D. Cooper, J. Graham, Active Shape Models—their training and applications, Computer Vision and Image Understanding, 61(1):38-59, 1995) satisfy this definition of segmentation schemes. The shape model is given by the principal components of vectors of landmark points. The gray-level appearance model describes the statistics of the normalized first derivative of profiles centered at each landmark that run perpendicular to the object contour. The location of a landmark in a new image is found by minimizing the Mahalanobis distance between the first derivative profile and the distribution of the profile. This algorithm starts from an initial estimate and performs a fitting procedure, which is an alternation of landmark displacements and shape model fitting. Similar approaches have been devised all employing a three-step procedure. First, they all use a shape model that ensures that plausible results are generated. Secondly, they use a gray-level appearance model to place the object at a location where the gray-level pattern around the border or within the object is similar to what is expected from the training examples. Finally, the algorithm fits the model by minimizing some cost function.